The Second Half of the Chessboard

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1 The Second Half of the Chessboard An ongoing exploration of transformative developments impacting the fast moving consumer goods retail industry Paper 2: In-Store Analytics Hit Prime Time About As Founder and CEO of CART, Gary Hawkins has an unparalleled view to current and future innovation in fast moving consumer goods retail. Reviewing thousands of new solutions each year, combined with over 30 years of industry experience leading shopper-focused innovation across the supply chain, uniquely position Hawkins to guide retail into the future. His work has been widely acknowledged by domestic and international publications including Harvard Business Review, the Financial Times, Elsevier, GDO Week, Mobile Commerce, and more. He has authored two internationally-published books, the Age of I white paper, the Retail 3.0 series of position papers and myriad articles. Hawkins is a regular guest lecturer at Georgetown University s McDonough School of Business in addition to keynoting retail conferences in the US and abroad. He can be reached at gary.hawkins@advancingretail.org The CART platform connects retailers, wholesalers, and brand manufacturers with solution providers, helping the industry keep pace with t o d a y s u n r e l e n t i n g innovation. Imagine you re in a hot air balloon tethered a hundred feet in the air over a supermarket. Now imagine that you re able to peel back the roof, providing you a bird s-eye view of the entire store. You re able to see shoppers coming into the parking lot, entering the store, perhaps picking up a shopping basket or getting a cart for their purchases. You then observe the shoppers as they move through the store, able to see what departments they go to, where they pause to consider a product, what aisles they go down - and which they don t. Now you lower your observation platform to hover over the store entrance, able to accurately count the number of people coming into the store. You re able to count men vs women, estimate age ranges, and even ethnicity. What s more, you can detect the mood of shoppers, knowing with a good confidence level if they are happy, sad, angry, or frustrated. Moving over to an aisle you seek to gain a more detailed view of what shoppers are doing at a given category. You re able to measure shopper traffic by the category, the number of shoppers who stop, see what products they pick up and if the product goes back on the shelf or into their basket before proceeding to checkout.

2 Wouldn t this be powerful information to have if you are a retailer? Knowing how shoppers move through your store, the impact - or lack thereof - of your merchandising displays? Which parts of the store are high traffic and which are not? The demographics of your shopper base? If you are a brand manufacturer, imagine having a true understanding of the impact of package design, the effectiveness of signage and other merchandising initiatives, and knowing where in the store your product was actually picked up - from the shelf, an end-of-aisle display, or off-shelf display. Consider the opportunities to collaborate with your retail customers using these insights; helping a retailer increase traffic in key areas of the store by leveraging your brands. In-store shopper analytics is quickly maturing, driven by a growing pipeline of new solutions coming into the market, decreasing cost of existing solutions, and increased understanding and actionability. Technology has digitized the in-store environment, providing deep insights, analytics and understanding of true shopper behavior in the store. Video analytics first appeared on the retail scene around 2008, early solution providers leveraging new digital cameras and increasingly powerful processing to convert video images to data points. While the insights video analytics are able to provide are powerful, its high cost - an estimated $100,000 to fully deploy a typical supermarket - have stymied its widespread adoption in consumer goods retail. Enter mobile analytics. Leading mobile analytic solutions are able to anonymously detect a shopper s mobile device upon entering and then track it around the store. Properly deployed sensors enable high levels of accuracy, enough to know which category the shopper is in front of. Mobile analytics are available at a fraction of the cost of video; New entrants into the space, with task-specific sensors, are changing the game, able to deploy a typical supermarket for less than $3,000. New capabilities are quickly reducing the cost to hundreds of dollars. New location technologies are flowing into the market: Graphic provided by Birdzi, Inc. one company is able to use the unique magnetic wave signature within each store to provide shopper location and at least two other companies are embedding codes into light, enabling lighting fixtures to support shopper location. Other companies are putting people-counting solutions on steroids. Available solutions are able to provide anonymous demographic data; counting male or female shoppers, estimating age ranges, and ethnicity. Making this even more powerful, at least one solution provider is able to report the shopper s mood, whether they are happy or sad, angry or frustrated, and so on. This company uses very advanced recognition software and the largest library of facial images in the world to provide this data. Understanding the importance of privacy, no video is retained, only the anonymous - yet powerful - analytics.

3 Stop and consider the power of this new data. A retailer now having a shoppermood scorecard, showing the number of shoppers by hour and by day who are happy, sad, angry, or frustrated. As a retailer I would correlate these shopper-mood scorecards with scheduling at service departments, seeking to understand any connection between a higher proportion of unhappy shoppers and the associates on at that time. Think about the opportunity to now grade store managers by the proportion of happy shoppers going out the door each week. New capabilities enable new ways to measure and manage business. Increasingly important as brick & mortar retailers seek to compete with the online merchants. Moving to a given category within the store, other solutions leverage Kinect-style sensors to provide a three-dimensional view of shopper behavior at the category. One particular solution provides directional traffic flowing by the category, reports on the number of shoppers who stop and dwell in front of the category, and then is able to provide analytics on what specific products the shopper picks up, from what shelf, and whether the product is put back on the shelf or in the shopper s basket. Think of these new measures as a funnel. At the top are all the shoppers entering the store. Some portion of them go to the produce Graphic provided by Shopperception, Inc. department, reported as a department conversion rate percentage. Some number of shoppers go to an aisle, then to a category; similar conversion rates. Some shoppers stop (dwell) in front of a particular category; a dwell conversion rate. Systems can measure that dwell event and report average dwell time. And lastly, linking these metrics with purchase data, we can measure and report a purchase conversion rate. Jon Kramer, CMO of WestRock Merchandising Displays, provides the following data to showcase the size of the opportunity to retailers and brands. The table shows the conversion rates for the deodorant and shaving category from a representative supermarket chain store. What we are seeing in this actual retail example is that nearly 25% of shoppers coming into the store go to (or by) the deodorant / shaving category. Of them, about 45% dwell, stopping to consider a purchase. Of those who dwell only 14.6% make an actual purchase. As the table shows, this represents a conversion opportunity of 85.4%: in other words, 85% of the shoppers who come to the category, dwell for some period of time pondering a purchase, then walk away, failing to make a purchase.

4 Why? Could they not find a product they were seeking? Did they not like the price? What a massive opportunity for improvement. And in the high volume / high velocity consumer goods environment, even small changes add up to significant revenue increases over the course of a year. But the learning and use of in-store analytics can go further. CART (advancingretail.org) conducted a several month long study in a live supermarket environment cross-referencing aisle traffic, conversion rates, purchase data, and promotion activity to gain even deeper insights: The study found very strong correlation between the advertising of certain specific cereal brands and higher aisle traffic in the pilot store. When Cheerios, Captain Crunch, and Life cereals were advertised, aisle traffic increased on average by 8%. Other brands had little to no impact. And, when shoppers purchased these key brands of cereal it was noticed that the sales of fruit juice, an adjacent product category, increased 9%. Consider the power of this insight - that specific brands within a category can drive significant increases in aisle traffic and purchases - and then extending that learning to key categories across the store. This opens a new dimension in promotion planning, incorporating the concept of choosing products for the weekly ad not just by price point but by their ability to drive conversion rates. Graphic provided by Birdzi, Inc. Leading-edge solution providers are bringing these analytics together in powerful platforms enabling retailers and brand manufacturers to not only gain new insights to their store environments but making those insights actionable. One of the leading mobile platforms ties together foot stream attributes (shopper s path through the store), aisle and category conversion rates, and shopper purchase history to enable the retailer to create campaigns to optimize store traffic. Precision-targeted offers, contextually relevant to the individual shopper, are communicated to the shopper s mobile based on realtime location in the store. Conclusion: Like website developers measuring how users click through a website, new technologies are digitizing physical stores; from click stream to foot stream. This ability for retailers and brands to understand how shoppers actually shop in the store, gain insight to the impact of merchandising and signage, and gain new measures of shopper behavior (conversion rates) represent game-changing capabilities.

5 Making these new insights and analytics actionable represents the leading edge of in-store marketing today. Adding in-store behavior to other big data such as detailed purchasing history creates the opportunity to strategically influence the shopper s path through the store using contextually relevant precision targeted promotions. As retailers and brands increase their understanding and use of these new tools, it provides powerful ammunition for brick & mortar retailers as the competition with online-only companies grows.